By Atri Mukherjee 

After a long day of work, you log on to Facebook or Instagram to catch up with trends within your digital circles. You use the application only for about ten minutes — nonchalantly “heart reacting” to a friend’s graduation boomerang, opening the link of an advertisement for a ‘cute Valentine’s day hoodie’, and quickly updating your story with an Arctic Monkeys’ song that has been stuck in your head all day long. Just as the scrolling starts getting boring, you call it a day, place your phone on the nightstand and go to sleep. 

Thousands of miles away from your living room, a massive server farm filled with wires and supercomputers begin crunching and converting your digital footprint into 0s and 1s, thereafter feeding it to powerful algorithms that show you the content you interact with every time you open a social media platform. 

The next morning, as you mindlessly scroll through your phone during the work day’s mandatory coffee break, not only does your Facebook or Instagram feed show you another romantic hoodie-gifting catalogue from a competing brand, but your Spotify also drops a notification saying “Hey, based on your listening history, we thought you’d like this —” with the album art of another Arctic Monkeys album on display. 

Now consider this exact transaction of your personal data in exchange for targeted digital content being replicated with billions of other people across the world. Add to this millions of such server farms (2,35,000 just in the top 3 — Intel, 1&1 Internet and OVH) and algorithms which tune themselves regularly to cater to ‘your needs’ and ask yourself this question: albeit being free and for all, who is the product and what is really being sold?

Facebook Technology India

Facebook Technology Monopoly
Facebook’s change in messaging on the login page | Source:

Netflix‘s “Social Dilemma” presented the case that this entire digital cycle — of collecting data, analysing it, and restructuring inferences to target certain services — is premised on internet users being the main product of interest. Every data point of a user is an infinitesimally small drop of fuel powering big tech’s engines. The longer you browse, the more data — in whatever regard, quantity or component — is available as an indelible part of their digital biodata. This cycle even goes beyond traditional ‘online browsing’ or social media; WhatsApp’s newest update that drew ire across India said that metadata from users could be used by affiliated platforms, such as Facebook for improved targeting. 

Whether one contributes their data voluntarily is hard to generalize. Instead, ask yourself this: if big tech is generating revenue off of collecting, analysing and restructuring our online activity, what can we ask for in return? Is it time that big tech paid users for collecting and using their individual data?

Of Monopolies and Monopsonies: There is no free lunch

What is important to note is the ever-increasing power and individualism of big tech. Facebook, Snapchat, YouTube, Twitter and such, control the production as well as the consumption of content on their platforms. Jaron Lanier and Eric Posner regard this incredible power of big tech companies as being both a monopoly and a monopsony. It is a monopoly in that its business model and market consolidation is enough to drive out most potential tech-driven entrepreneurial start-ups out of the fringe borders of Silicon Valley. 

It is in the understanding of cultural monopsonies that the process of data extraction and redistribution that we realize how little it is that is there for all its users, and how most of a user’s activities reap much more substantive benefits for those in the big chairs of Silicon Valley high-rises. For the user’s case, the ‘revenue’ is considered in the terms of apparent free use of Silicon Valley’s free-to-use applications. This dynamic — where the user’s data-based labour is supposedly offered in goodwill — is an example of a cultural monopsony that has managed to coexist in the data markets even after the talks of saviour capitalism. 

What is a cultural monopsony? Let’s use Posner’s example: 

If some enthusiasts came together to play cricket in a public playground, should the audience that is around them pay to watch them play? Since it is a public playground, the audience has no legal implication to pay. However, what if the cricket players demanded a pay calculated to the rupee-equivalent of their most minimal contribution to the process of cricket-playing? Could such a system produce more social gains, wherein better players started coming to the field? The idea here is that the audience is very likely to frown at any player who would demand financial compensation to their leisurely play. 

This is the same case with women’s domestic work, college-funded sports (with underpaid players) and other such markets where the value of labour is disregarded as being a benefit for the person doing the labour. What is more interesting is that this labour is regarded as wholly independent of the larger processes that make the tech markets.

But here’s an interesting counterpoint to the concept of monopsonies:  

With a candid disclosure of not being “a tech person”, but one “who has lived and learned through the internet over the past decade”, Devaiah Bopanna tells us about his simple approach to the trade-off with big tech vis-a-vis our data. The writer and co-founder of All Things Small tells us,

I see a lot of ‘liberals’ jumping on this [privacy] bandwagon, saying ‘Hey, don’t take our data!’… but how can you forget that all of these [tech] companies are making our lives so much more efficient and convenient? You have built your businesses online, profited off of social media marketing, and your life is connected to the rest of the world because of the innovations brought about by Gmail, or Instagram. Then, how can you possibly justify using someone else’s ideas, resources and time for free?

For Bopanna, there is no discussion about big tech paying users for their data, simply because there exists no free lunch. Data-sharing is one of the biggest non-negotiable rules of the game. “Just imagine owning a company today but not using the messaging services provided by WhatsApp. Are you going to SMS people?”, he asks. If networking and communication platforms like email or Facebook were paywalled, it would restrict the number of people who could use them, stifle innovation and restrict the accessibility that has almost become synonymous to the internet. Some would go so far to say that it would violate human rights! Instead, you pay for using these platforms and applications with your individual (and collective) data points. 

 Bopanna’s approach would find resistance from the likes of Jaron Lanier and Glen Weyl, who argue that providing one’s data or activity is a form of involuntary labour that is met with no financial compensation for the people who are essentially doing the brick-laying for all of big tech’s capabilities. It has created a market economy based on information, that Weyl calls ‘liberal radicalism’ or ‘data as labour’. 

If we consider the effort of the cricketers from the earlier example to that of users, calculated at the most minimal rupee-equivalent, how would the valuation of that data stand? Do users get to choose the price of each type of data they part with, and would they be able to exchange it with a big tech company of their choice? 

Cricketers Need Managers: Cue Mediators of Individual Data

As with the concept of Creative Commons and digital content, if the producer of data (i.e., the user) were able to have a say in the flow of their data, it could be negotiated to be sold to the highest prospective bidder. The problem with implementing such a scenario lies in the political and sociological implications of it. 

Politically, attempts such as Andrew Yang’s ‘Data Dividend Project’ end up oversimplifying how data management should work with producers, middlemen and final buyers. It makes little sense for politicians to campaign on this behalf, in the face of rich capitalists. The sociological problem, on the other hand, is in the branding of paying for data: it sounds like a fever dream conjured by beardy seven-eleven socialists. 

 Mediators of Individual Data (MIDs), can make this process significantly smoother for all parties.

What is an MID? With big tech’s multiple revenue systems, it is impossible for individual users to demand data dignity on their own. An individual user will seldom be able to have any sort of leveraging dialogue with owners of a big digital platform. MIDs aim to catalyze this exact communication gap between users and big tech. A Mediator of Individual Data is a body with its own set of governance strategies, rules and departments, that will seek to represent its clients (i.e., internet users) in many ways within the virtual trade-offs of data valuation. It will act as the middleman between the user and big tech, and negotiate with the latter to sell the data of the user at the highest common valuation of their produced data. Once big tech buys this data, it would henceforth be eligible to target/analyse desired digital markets. 

Simply put, MIDs will push back against the centralized stronghold that big tech has over a user’s personal data, thereby letting the producer manage and monetize their data effectively. Using the analogy of cricketers in a public playground, Mediators of Individual Data would act like the players’ managers.

But do users really value their privacy? 

How would MIDs even begin valuing a certain data point? Are some users more valuable than others? “Most importantly, how much do people actually value their privacy?” asks Bopanna. “When push comes to shove, ‘not very much’ would be my answer”, he laughs. While it may be unwise to generalize, this prioritisation of privacy is a major blind spot in any proposal for ‘data dignity’ or financially compensating a user.

“If [dissenters] valued their privacy, they would not be signing up [on social media] in the first place. If there is someone putting out these long rants about firms using their data, they should have the basic common sense to read the terms and conditions. Most people sign up knowing fully well that Facebook is making money through their data — this isn’t something that began yesterday. Using data for commerce is fine, but not for jeopardising democracy”, says Bopanna. 

At least in an Indian context, Bopanna seems to be referring to a very small section of internet users. Digital illiteracy (or ignorance) of the larger processes behind data collection, and the role users play, is an immense challenge. The Digital Empowerment Foundation reported in 2018 that over 90% of India’s population had no digital literacy.  

Is a digitally illiterate population capable of adding a financial value to the data they produce and the benefits accrued through third-party involvement? MIDs — in tandem with projects such as The National Digital Literacy Mission and the Pradhan Mantri Gramin Digital Saksharta Abhiyan —  can work to raise awareness and defend users data, refocusing on the oft-overlooked and understated presence of digital monopsonies.

The Bargain for India and Her Users

This concept of paying for data is not only limited to conversations in the parking lots of Silicon Valley. Byrne and Fernald from the Federal Reserve Bank of the United States and Reisdorf from the International Monetary Fund (IMF) have conducted studies which show how a purely free data economy is not lucrative and stagnates the productivity of the worldwide markets working on data, despite there being significant scope to monetise this arena. In their book ‘Radical Markets’, Weyl and Posner argue that putting a price on collected data can raise the median middle-income bracket for the United States. 

But what would the conversation look like in an Indian context? One thing is for sure: there exists a massive potential for a data economy just by virtue of the user base. Not only will MIDs represent a sizable digitally illiterate population better, but they can also mentor them on better web navigation and data valuation. Hailed across the world, the emergence and popularity of UPI has set the framework for paying users who have only a smartphone. 

However, as with most debates concerning big tech’s increasing power, one side of the aisle prefers to maintain the status quo, while the other hopes for some sense of a digital revolution (and revaluation). Notwithstanding these, there are some huge and difficult-to-answer questions that need answering — how would we effectively value data? Is there a uniform slab rate for all data across the board, or is some data more valuable than others? With unethical data extraction and exploitation, even with big tech, would there be possibilities of fraudulence and data theft so big tech pays less? More importantly, what role would governments pay in the new system?

We attempt to explore these, in an Indian context, in part two of this series. Until then, consider the notion of being remunerated for using Instagram or Facebook, however small an amount that may be.



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